ABSTRACT
Genocide scholarship is underpinned by an implicit revulsion at the suffering, violence and degradation perpetrated by human beings against other human beings. Those of us who work in the field may deploy different methodologies, standpoints and frames of reference by which we seek to understand both the causes and consequences of mass violence as inflicted on whole groups of people across historical time. Implicitly, where not explicitly, we are impelled in what we do by a desire to live in a world where genocide and all such crimes against humanity have been consigned to the past. This statement, as predicated on aspirations for a kinder, gentler commonwealth, is rendered inoperable, however, so long as we seek to ignore or avoid the biospheric crisis facing humankind. A generation on from when anthropogenic climate change became general knowledge, nearly all of human society – usually excepting those most directly involved either as earth science practitioners, or as environmental victims – have been too slow in recognizing or acknowledging the far-reaching and destructive scale of the biogeochemical disruption as it will impact on our lives and wellbeing. The present coronavirus pandemic in these terms is simply a signal warning from nature reminding us that ever increasing human disturbance to an already threadbare ecological balance must in turn have severe consequences for ourselves. Yet the overall effect of human-induced planetary destablization in coming decades will make of one singular zoonotic event a passing footnote. The bigger picture is one in which the thresholds allowing for our sustainable, cross-generational flourishing are in the process of being breached at an alarming and exponentially escalating rate. At the core of this ongoing ecological collapse is the rapid heating of the planet as a result of the vast quantities of fossil fuels some of us – primarily in the Global North – are burning and thus emitting as greenhouse gases into the atmosphere. The consequential breakdown of sustainable food production and the permanent salination or inundation of the land much of us inhabit, alongside the direct effects of soaring, unbearable temperatures, will lead to the displacement and death of hundreds of millions, if not billions of human beings. Yet this apocalypse in the making is not some unforeseen blip or caesura. On the contrary, its causes can be traced to the same forces which created the conditions for modernity, not least through colonial conquest and predation and with them the arrival of a hegemonic world system. The interrelationship thus between ecocidal and genocidal warfare, waged against peoples and planet is built into this anthropocenic turn.
ABSTRACT
A novel yet simple extension of the symmetric logistic distribution is proposed by introducing a skewness parameter. It is shown how the three parameters of the ensuing skew logistic distribution may be estimated using maximum likelihood. The skew logistic distribution is then extended to the skew bi-logistic distribution to allow the modelling of multiple waves in epidemic time series data. The proposed skew-logistic model is validated on COVID-19 data from the UK, and is evaluated for goodness-of-fit against the logistic and normal distributions using the recently formulated empirical survival Jensen-Shannon divergence (ESJS) and the Kolmogorov-Smirnov two-sample test statistic (KS2). We employ 95% bootstrap confidence intervals to assess the improvement in goodness-of-fit of the skew logistic distribution over the other distributions. The obtained confidence intervals for the ESJS are narrower than those for the KS2 on using this dataset, implying that the ESJS is more powerful than the KS2.
ABSTRACT
BACKGROUND: The COVID-19 pandemic has created a pressing need for integrating information from disparate sources in order to assist decision makers. Social media is important in this respect; however, to make sense of the textual information it provides and be able to automate the processing of large amounts of data, natural language processing methods are needed. Social media posts are often noisy, yet they may provide valuable insights regarding the severity and prevalence of the disease in the population. Here, we adopt a triage and diagnosis approach to analyzing social media posts using machine learning techniques for the purpose of disease detection and surveillance. We thus obtain useful prevalence and incidence statistics to identify disease symptoms and their severities, motivated by public health concerns. OBJECTIVE: This study aims to develop an end-to-end natural language processing pipeline for triage and diagnosis of COVID-19 from patient-authored social media posts in order to provide researchers and public health practitioners with additional information on the symptoms, severity, and prevalence of the disease rather than to provide an actionable decision at the individual level. METHODS: The text processing pipeline first extracted COVID-19 symptoms and related concepts, such as severity, duration, negations, and body parts, from patients' posts using conditional random fields. An unsupervised rule-based algorithm was then applied to establish relations between concepts in the next step of the pipeline. The extracted concepts and relations were subsequently used to construct 2 different vector representations of each post. These vectors were separately applied to build support vector machine learning models to triage patients into 3 categories and diagnose them for COVID-19. RESULTS: We reported macro- and microaveraged F1 scores in the range of 71%-96% and 61%-87%, respectively, for the triage and diagnosis of COVID-19 when the models were trained on human-labeled data. Our experimental results indicated that similar performance can be achieved when the models are trained using predicted labels from concept extraction and rule-based classifiers, thus yielding end-to-end machine learning. In addition, we highlighted important features uncovered by our diagnostic machine learning models and compared them with the most frequent symptoms revealed in another COVID-19 data set. In particular, we found that the most important features are not always the most frequent ones. CONCLUSIONS: Our preliminary results show that it is possible to automatically triage and diagnose patients for COVID-19 from social media natural language narratives, using a machine learning pipeline in order to provide information on the severity and prevalence of the disease for use within health surveillance systems.